s2_tetl_firststopstorebeac = np.zeros((len(days), len(mice),2));s2_tetl_firststopstorenbeac = np.zeros((len(days), len(mice),2));s2_tetl_firststopstoreprobe = np.zeros((len(days), len(mice),2))
s2_tetl_firststopstorebeac[:,:,:] = np.nan;s2_tetl_firststopstorenbeac[:,:,:] = np.nan; s2_tetl_firststopstoreprobe[:,:,:] = np.nan
s2_tetl_firststopstorebeac_s = np.zeros((len(days), len(mice),2));s2_tetl_firststopstorenbeac_s = np.zeros((len(days), len(mice),2));s2_tetl_firststopstoreprobe_s = np.zeros((len(days), len(mice),2))
s2_tetl_firststopstorebeac_s[:,:,:] = np.nan;s2_tetl_firststopstorenbeac_s[:,:,:] = np.nan; s2_tetl_firststopstoreprobe_s[:,:,:] = np.nan
s2_teth_firststopstorebeac = np.zeros((len(days), len(mice),2));s2_teth_firststopstorenbeac = np.zeros((len(days), len(mice),2));s2_teth_firststopstoreprobe = np.zeros((len(days), len(mice),2))
s2_teth_firststopstorebeac[:,:,:] = np.nan;s2_teth_firststopstorenbeac[:,:,:] = np.nan; s2_teth_firststopstoreprobe[:,:,:] = np.nan
s2_teth_firststopstorebeac_s = np.zeros((len(days), len(mice),2));s2_teth_firststopstorenbeac_s = np.zeros((len(days), len(mice),2));s2_teth_firststopstoreprobe_s = np.zeros((len(days), len(mice),2))
s2_teth_firststopstorebeac_s[:,:,:] = np.nan;s2_teth_firststopstorenbeac_s[:,:,:] = np.nan; s2_teth_firststopstoreprobe_s[:,:,:] = np.nan
#loop days and mice to collect data
for mcount,mouse in enumerate(mice):
for dcount,day in enumerate(days):
try:
saraharray = readhdfdata(filename,day,mouse,'raw_data')
except KeyError:
print ('Error, no file')
continue
# make array of trial number per row of data in dataset
trialarray = maketrialarray(saraharray) # make array of trial number same size as saraharray
saraharray[:,9] = trialarray[:,0] # replace trial number because of increment error (see README.py)
# split data by trial type
dailymouse_b = np.delete(saraharray, np.where(saraharray[:, 8] > 0), 0) # delete all data not on beaconed tracks
dailymouse_nb = np.delete(saraharray, np.where(saraharray[:, 8] != 10), 0)# delete all data not on non beaconed tracks
dailymouse_p = np.delete(saraharray, np.where(saraharray[:, 8] != 20), 0)# delete all data not on probe tracks
#extract stops
stopsdata_b = extractstops(dailymouse_b)
stopsdata_p = extractstops(dailymouse_p)
filename = 'Data_Input/Behaviour_DataFiles/Task13_0300.h5'
#specify mouse/mice and day/s to analyse
#days = ['Day' + str(int(x)) for x in np.arange(1,21.1)] # Several days
#mice = ['M' + str(int(x)) for x in np.arange(1,8.1)] # Several mice
mice = ['M' + str(int(x)) for x in [6]]# specific mice
days = ['Day' + str(int(x)) for x in [1,17]]# specific day/s
bins = np.arange(0.5,20.5,1) # array of bins for location
# For each day and mouse, pull raw data, calculate stops/speed and plot graph
for dcount,day in enumerate(days):
for mcount,mouse in enumerate(mice):
print ('Processing...',day,mouse)
try:
saraharray = readhdfdata(filename,day,mouse,'raw_data')#load HDF5 data set for that day and mouse
except KeyError: # if there is no datafile, skip that mouse & day
print ('Error, no file')
continue
trialno = np.max(saraharray[:,9]) # total number of trials for that day and mouse (used later for defining y axis max)
# make array of trial number for each row in dataset
trialarray = maketrialarray(saraharray)
saraharray[:,9] = trialarray[:,0] # replace trial column in dataset *see README for why this is done*
# Extract data for beaconed, non-beaconed, probe
dailymouse_b = np.delete(saraharray, np.where(saraharray[:, 8] > 0), 0) # delete all data not on beaconed tracks
dailymouse_nb = np.delete(saraharray, np.where(saraharray[:, 8] != 10), 0)# delete all data not on non beaconed tracks
dailymouse_p = np.delete(saraharray, np.where(saraharray[:, 8] != 20), 0)# delete all data not on probe tracks
#extract stops
firststopstorebeac[:, :] = np.nan
firststopstorenbeac[:, :] = np.nan
firststopstoreprobe[:, :] = np.nan
firststopstorebeac_s = np.zeros((len(days), len(mice)))
firststopstorenbeac_s = np.zeros((len(days), len(mice)))
firststopstoreprobe_s = np.zeros((len(days), len(mice)))
firststopstorebeac_s[:, :] = np.nan
firststopstorenbeac_s[:, :] = np.nan
firststopstoreprobe_s[:, :] = np.nan
# For each day and mouse, pull raw data, calculate zscore and store data
for mcount, mouse in enumerate(mice):
for dcount, day in enumerate(days):
try:
saraharray = readhdfdata(
filename, day, mouse,
'raw_data') # get raw datafile for mouse and day
except KeyError:
print('Error, no file')
continue
# make array of trial number for each row in dataset
trialarray = maketrialarray(
saraharray) # write array of trial per row in datafile
saraharray[:,
9] = trialarray[:,
0] # replace trial column in dataset *see README for why this is done*
# get stops and trial arrays
dailymouse_b = np.delete(saraharray, np.where(saraharray[:, 8] > 0),
0) # delete all data not on beaconed tracks
